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Multi-task metaphor detection based on linguistic theory

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Abstract

Metaphorical expressions are widely present in natural language, posing significant challenges to a variety of natural language processing tasks such as machine translation. How to obtain richer contextual representations is an urgent problem to be solved. To address this issue, this paper proposes a model that combines syntax-aware local attention (SLA), a simple contrastive sentence embedding framework (SimCSE), and linguistic theories, called a combination of syntax-aware and semantic methods (CSS). Specifically, we apply linguistic theory in metaphor detection. Additionally, we simultaneously conduct metaphor identification and contrastive learning tasks. The SimCSE contrastive learning framework effectively captured more information, and the concurrent execution of these two tasks helped increase the sensitivity of the semantic space to metaphors. The integration of SLA with the pre-trained language model BERT enhanced the attention weights between grammatically relevant words, assisting the encoder in focusing more on grammar-related words. Overall, CSS prioritizes the sentence itself, avoiding the introduction of excessive additional information. Experimental results on the VU Amsterdam-verb (VUA), TroFi, and MOH-X metaphorical corpora show that our method is superior to state-of-the-art models.

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Data availability

These data were derived from the following resources available in the public domain: [http://ota.ahds.ac.uk/headers/2541.xml, http://saifmohammad.com/WebPages/metaphor.html, http://natlang.cs.sfu.ca/software/trofi.html].

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Acknowledgements

This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant numbers: 2023D01C176), Tianshan yingcai peiyang (Grant numbers: 2023TSYCLJ), Xinjiang Uygur Autonomous Region Universities Fundamental Research Funds Scientific Research Project (Grant numbers: XJEDU2022P018), National Natural Science Foundation of China (Grant numbers: 61962057), Key Program of National Natural Science Foundation of China (Grant numbers: U2003208), Major science and technology projects in the autonomous region, China (Grant numbers: 2020A03004-4), Key research and development projects in the autonomous region (Grant numbers: 2021B01002).

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Authors and Affiliations

  1. College of Software, Xinjiang University, Urumqi, 830000, China

    Ziqi Song, Shengwei Tian, Xiaoyu Zhang & Jing Liu

  2. Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China

    Ziqi Song, Shengwei Tian, Xiaoyu Zhang & Jing Liu

  3. College of Network Center, Xinjiang University, Urumqi, 830000, China

    Long Yu

  4. Signal and Signal Processing Laboratory, College of Information Science and Engineering, Xinjiang University, Urumqi, 830000, China

    Long Yu

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  1. Ziqi Song
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  2. Shengwei Tian
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  3. Long Yu
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  4. Xiaoyu Zhang
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  5. Jing Liu
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Contributions

Ziqi Song: Conceptualization, Methodology, Writing-original draft, Software, Writing-review & editing. Shengwei Tian: Supervision. Long Yu: Supervision. Xiaoyu He: Supervision, Validation. Jing Liu: Data Curation, Validation.

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Correspondence to Shengwei Tian.

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Song, Z., Tian, S., Yu, L. et al. Multi-task metaphor detection based on linguistic theory. Multimed Tools Appl 83, 64065–64078 (2024). https://doi.org/10.1007/s11042-023-18063-1

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